Design and validation of realistic breast models for use in multiple alternative forced choice virtual clinical trials

A novel method has been developed for generating quasi-realistic voxel phantoms which simulate the compressed breast in mammography and digital breast tomosynthesis (DBT). The models are suitable for use in virtual clinical trials requiring realistic anatomy which use the multiple alternative forced choice (AFC) paradigm and patches from the complete breast image. The breast models are produced by extracting features of breast tissue components from DBT clinical images including skin, adipose and fibro-glandular tissue, blood vessels and Cooper's ligaments. A range of different breast models can then be generated by combining these components. Visual realism was validated using a receiver operating characteristic (ROC) study of patches from simulated images calculated using the breast models and from real patient images. Quantitative analysis was undertaken using fractal dimension and power spectrum analysis. The average areas under the ROC curves for 2D and DBT images were 0.51  ±  0.06 and 0.54  ±  0.09 demonstrating that simulated and real images were statistically indistinguishable by expert breast readers (7 observers); errors represented as one standard error of the mean. The average fractal dimensions (2D, DBT) for real and simulated images were (2.72  ±  0.01, 2.75  ±  0.01) and (2.77  ±  0.03, 2.82  ±  0.04) respectively; errors represented as one standard error of the mean. Excellent agreement was found between power spectrum curves of real and simulated images, with average β values (2D, DBT) of (3.10  ±  0.17, 3.21  ±  0.11) and (3.01  ±  0.32, 3.19  ±  0.07) respectively; errors represented as one standard error of the mean. These results demonstrate that radiological images of these breast models realistically represent the complexity of real breast structures and can be used to simulate patches from mammograms and DBT images that are indistinguishable from patches from the corresponding real breast images. The method can generate about 500 radiological patches (~30 mm  ×  30 mm) per day for AFC experiments on a single workstation. This is the first study to quantitatively validate the realism of simulated radiological breast images using direct blinded comparison with real data via the ROC paradigm with expert breast readers.

[1]  Christoph Hoeschen,et al.  A high-resolution voxel phantom of the breast for dose calculations in mammography. , 2005, Radiation protection dosimetry.

[2]  Andrew D. A. Maidment,et al.  Optimized generation of high resolution breast anthropomorphic software phantoms. , 2012, Medical physics.

[3]  R. Siddon.  Fast calculation of the exact radiological path for a three-dimensional CT array. , 1985, Medical physics.

[4]  Yoon Jung Choi,et al.  Mammographic Breast Density Evaluation in Korean Women Using Fully Automated Volumetric Assessment , 2016, Journal of Korean medical science.

[5]  J. Boone,et al.  The effect of skin thickness determined using breast CT on mammographic dosimetry. , 2008, Medical physics.

[6]  D. Kopans,et al.  Digital tomosynthesis in breast imaging. , 1997, Radiology.

[7]  A. Burgess.  Comparison of receiver operating characteristic and forced choice observer performance measurement methods. , 1995, Medical physics.

[8]  Hilde Bosmans,et al.  Development and validation of a modelling framework for simulating 2D-mammography and breast tomosynthesis images , 2014, Physics in medicine and biology.

[9]  Sabine Siesling,et al.  Recent trends of cancer in Europe: a combined approach of incidence, survival and mortality for 17 cancer sites since the 1990s. , 2008, European journal of cancer.

[10]  Kenneth C. Young,et al.  A fast scatter field estimator for digital breast tomosynthesis , 2012, Medical Imaging.

[11]  Andrew D. A. Maidment,et al.  Development and characterization of an anthropomorphic breast software phantom based upon region-growing algorithm. , 2011, Medical physics.

[12]  H. Menzel,et al.  The International Commission on Radiation Units and Measurements , 2011, Journal of the ICRU.

[13]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[14]  D R Dance,et al.  Simulation and assessment of realistic breast lesions using fractal growth models , 2013, Physics in medicine and biology.

[15]  S Suryanarayanan,et al.  Evaluation of an improved algorithm for producing realistic 3D breast software phantoms: application for mammography. , 2010, Medical physics.

[16]  J. S. Laughlin,et al.  Absorbed radiation dose in mammography. , 1979, Radiology.

[17]  Robert M. Nishikawa,et al.  A Directional Small-Scale Tissue Model for an Anthropomorphic Breast Phantom , 2012, Digital Mammography / IWDM.

[18]  James A. Scott.  Photon, Electron, Proton and Neutron Interaction Data for Body Tissues ICRU Report 46. International Commission on Radiation Units and Measurements, Bethesda, 1992, $40.00 , 1993 .

[19]  J. Michael O'Connor,et al.  Comparison of Two Methods to Develop Breast Models for Simulation of Breast Tomosynthesis and CT , 2008, Digital Mammography / IWDM.

[20]  R Strickland,et al.  An approach to the synthesis of biological tissue. , 1997, Optics express.

[21]  Christian G. Graff,et al.  A new, open-source, multi-modality digital breast phantom , 2016, SPIE Medical Imaging.

[22]  Ioannis Sechopoulos,et al.  BreastSimulator: A software platform for breast x-ray imaging research , 2012 .

[23]  D. Geddes,et al.  Ultrasound imaging of the lactating breast: methodology and application , 2009, International breastfeeding journal.

[24]  Nico Karssemeijer,et al.  Robust Breast Composition Measurement - VolparaTM , 2010, Digital Mammography / IWDM.

[25]  Lorraine Tucker,et al.  Digital breast tomosynthesis (DBT): a review of the evidence for use as a screening tool. , 2016, Clinical radiology.

[26]  Kenneth C. Young,et al.  OPTIMAM Image Simulation Toolbox - Recent Developments and Ongoing Studies , 2016, Digital Mammography / IWDM.

[27]  Andrew D. A. Maidment.  Virtual Clinical Trials for the Assessment of Novel Breast Screening Modalities , 2014, Digital Mammography / IWDM.

[28]  Susan M. Astley,et al.  Classification of Breast Tissue by Texture Analysis , 1991, BMVC.

[29]  Felix Diekmann,et al.  Tomosynthesis and contrast-enhanced digital mammography: recent advances in digital mammography , 2007, European Radiology.

[30]  Michael Sandborg,et al.  Implementation of pathologies in the Monte Carlo model in chest and breast imaging , 2003 .

[31]  Oliver Diaz,et al.  Image simulation and a model of noise power spectra across a range of mammographic beam qualities. , 2014, Medical physics.

[32]  M. Yaffe,et al.  Characterisation of mammographic parenchymal pattern by fractal dimension. , 1990, Physics in medicine and biology.

[33]  Kyle J Myers,et al.  A virtual trial framework for quantifying the detectability of masses in breast tomosynthesis projection data. , 2013, Medical physics.

[34]  N. Erdoğan,et al.  Effect of age, breast size, menopausal and hormonal status on mammographic skin thickness , 2003, Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging.

[35]  Kenneth C. Young,et al.  Comparison of Breast Doses for Digital Tomosynthesis Estimated from Patient Exposures and Using PMMA Breast Phantoms , 2012, Digital Mammography / IWDM.

[36]  Arthur E. Burgess,et al.  Mammographic structure: data preparation and spatial statistics analysis , 1999, Medical Imaging.

[37]  Andrew D. A. Maidment,et al.  Realistic Simulation of Breast Tissue Microstructure in Software Anthropomorphic Phantoms , 2014, Digital Mammography / IWDM.

[38]  T. R. Fewell,et al.  Molybdenum, rhodium, and tungsten anode spectral models using interpolating polynomials with application to mammography. , 1997, Medical physics.

[39]  Mehran Ebrahimi,et al.  Anatomical noise in contrast-enhanced digital mammography. Part I. Single-energy imaging. , 2013, Medical physics.

[40]  John M Boone,et al.  Methodology for generating a 3D computerized breast phantom from empirical data. , 2009, Medical physics.

[41]  H Bosmans,et al.  Comparative power law analysis of structured breast phantom and patient images in digital mammography and breast tomosynthesis. , 2013, Medical physics.

[42]  Andrew D. A. Maidment,et al.  Mammogram synthesis using a 3D simulation. II. Evaluation of synthetic mammogram texture. , 2002, Medical physics.

[43]  Kenneth C. Young,et al.  Conversion of mammographic images to appear with the noise and sharpness characteristics of a different detector and x-ray system. , 2012, Medical physics.

[44]  R P Velthuizen,et al.  On the statistical nature of mammograms. , 1999, Medical physics.

[45]  Kwan-Hoong Ng,et al.  Vision 20/20: Mammographic breast density and its clinical applications. , 2015, Medical physics.

[46]  C Abbey,et al.  Statistical texture synthesis of mammographic images with super-blob lumpy backgrounds. , 1999, Optics express.

[47]  Pierre-Edouard Sottas,et al.  Mammographic texture synthesis: second-generation clustered lumpy backgrounds using a genetic algorithm. , 2008, Optics express.

[48]  Aruna A. Vedula,et al.  A computer simulation study comparing lesion detection accuracy with digital mammography, breast tomosynthesis, and cone-beam CT breast imaging. , 2006, Medical physics.

[49]  Andrew D. A. Maidment,et al.  Mammogram synthesis using a 3D simulation. I. Breast tissue model and image acquisition simulation. , 2002, Medical physics.

[50]  Nooshin Kiarashi,et al.  The impact of breast structure on lesion detection in breast tomosynthesis , 2015, Medical Imaging.

[51]  Alaleh Rashidnasab,et al.  Simulation of 3D DLA masses in digital breast tomosynthesis , 2013, Medical Imaging.

[52]  E Shaheen,et al.  Simulation of 3D objects into breast tomosynthesis images. , 2010, Radiation protection dosimetry.

[53]  Yit Yoong Lim,et al.  The TOMMY trial: a comparison of TOMosynthesis with digital MammographY in the UK NHS Breast Screening Programme--a multicentre retrospective reading study comparing the diagnostic performance of digital breast tomosynthesis and digital mammography with digital mammography alone. , 2015, Health technology assessment.

[54]  D R Dance,et al.  Estimation of scattered radiation in digital breast tomosynthesis , 2014, Physics in medicine and biology.

[55]  Nooshin Kiarashi,et al.  A quantitative metrology for performance characterization of five breast tomosynthesis systems based on an anthropomorphic phantom. , 2016, Medical physics.

[56]  Petrus V. Deventer,et al.  The Blood Supply to the Nipple-Areola Complex of the Human Mammary Gland , 2004 .

[57]  Nooshin Kiarashi,et al.  Impact of breast structure on lesion detection in breast tomosynthesis, a simulation study , 2016, Journal of medical imaging.

[58]  Mousumi Banerjee,et al.  Reduction in late‐stage breast cancer incidence in the mammography era: Implications for overdiagnosis of invasive cancer , 2014, Cancer.

[59]  K Bliznakova,et al.  A three-dimensional breast software phantom for mammography simulation. , 2003, Physics in medicine and biology.